Latest AI and machine learning research in lung cancer for healthcare professionals.
Accurate localization of tumor regions from hematoxylin and eosin-stained whole-slide images is fund...
Predicting tumor evolution during radiotherapy is a clinically critical challenge, particularly when...
Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning t...
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by profound molecu...
The application of large vision-language models to computational pathology holds great promise for d...
Background: Kidney volumetry derived from CT has been proposed as a surrogate of renal function in l...
Cancer driver mutations shape the tumor microenvironment (TME), yet whether TME composition alone ca...
The image purification strategy constructs an intermediate distribution with aligned anatomical stru...
Computed Tomography (CT) is one of the largest contributors to radiation exposure from medical imagi...
Radiogenomics enables the non invasive characterisation of the genomic and molecular properties of t...
Background: Pancreatic ductal adenocarcinoma is one of the most aggressive and lethal malignancies o...
Lung cancer is the leading cause of cancer-related mortality worldwide, predominantly affects older ...
IMPORTANCE: Although angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor block...
Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention b...
Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, largely due to the abs...
DNA methylation is a central epigenetic modification that regulates gene expression, maintains genom...
Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest solid malignancies, is often detected ...
Multimodal evidence is critical in computational pathology: gigapixel whole slide images capture tum...
Protein expression within oncogenic or suppressive pathways is a hallmark indicator of oncogenesis. ...
Accurate prediction of mutational dependencies to model tumor evolution can improve our understandin...
Deep learning models in computational pathology often fail to generalize across cohorts and institut...